Customer segmentation is the process of dividing the customers into groups based on common characterstics so that companies can understand and market to each group effectively and appropriately to understand their choices and preferences.

The purpose of segmenting customers is to determine how to correlate to customers to maximize their benefits. Perfectly done customer segmentation empowers marketers to interact with every customer in the best efficient approach

The dataset includes:

Customer ID Gender Age Annual Income Spending Score

Import libraries and Load the dataset

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from sklearn.preprocessing import StandardScaler

import warnings
warnings.filterwarnings('ignore')

data = pd.read_csv('Mall_Customers.csv')
data

	CustomerID	Gender	Age	Annual Income (k$)	Spending Score (1-100)
0	1	Male	19	15	39
1	2	Male	21	15	81
2	3	Female	20	16	6
3	4	Female	23	16	77
4	5	Female	31	17	40
...	...	...	...	...	...
195	196	Female	35	120	79
196	197	Female	45	126	28
197	198	Male	32	126	74
198	199	Male	32	137	18
199	200	Male	30	137	83

200 rows × 5 columns

Data Exploration in order to understand the data

data.head()   #displays first few rows to understand the data

	CustomerID	Gender	Age	Annual Income (k$)	Spending Score (1-100)
0	1	Male	19	15	39
1	2	Male	21	15	81
2	3	Female	20	16	6
3	4	Female	23	16	77
4	5	Female	31	17	40

data.shape #shape is used to display the number of rows and columns. Here we have 200 rows and 5 columns

(200, 5)

data.info() #info is used to ger non-null count, datatypes of each feature

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 200 entries, 0 to 199
Data columns (total 5 columns):
 #   Column                  Non-Null Count  Dtype 
---  ------                  --------------  ----- 
 0   CustomerID              200 non-null    int64 
 1   Gender                  200 non-null    object
 2   Age                     200 non-null    int64 
 3   Annual Income (k$)      200 non-null    int64 
 4   Spending Score (1-100)  200 non-null    int64 
dtypes: int64(4), object(1)
memory usage: 7.9+ KB

data.describe()   #describe is used to get summary statistics like count, mean, max, min, std

	CustomerID	Age	Annual Income (k$)	Spending Score (1-100)
count	200.000000	200.000000	200.000000	200.000000
mean	100.500000	38.850000	60.560000	50.200000
std	57.879185	13.969007	26.264721	25.823522
min	1.000000	18.000000	15.000000	1.000000
25%	50.750000	28.750000	41.500000	34.750000
50%	100.500000	36.000000	61.500000	50.000000
75%	150.250000	49.000000	78.000000	73.000000
max	200.000000	70.000000	137.000000	99.000000

data.value_counts()

CustomerID  Gender  Age  Annual Income (k$)  Spending Score (1-100)
1           Male    19   15                  39                        1
138         Male    32   73                  73                        1
128         Male    40   71                  95                        1
129         Male    59   71                  11                        1
130         Male    38   71                  75                        1
                                                                      ..
70          Female  32   48                  47                        1
71          Male    70   49                  55                        1
72          Female  47   49                  42                        1
73          Female  60   50                  49                        1
200         Male    30   137                 83                        1
Length: 200, dtype: int64

data['Gender'].describe()

count        200
unique         2
top       Female
freq         112
Name: Gender, dtype: object

data['Gender'].value_counts()

Female    112
Male       88
Name: Gender, dtype: int64

Data Visualization on Orginial dataset

x = data['Age']
y = data['Annual Income (k$)']
plt.scatter(x,y,s=50,c="black")
plt.title("Age and their annual incomes")
plt.xlabel("Age")
plt.ylabel("Annual Income") #We can clearely see that person with age 20 has got less annual income and person with age 30 has 
                            #got more annual income

Text(0, 0.5, 'Annual Income')

plt.figure(figsize=(13,5))
sns.countplot(x =data['Age'])
plt.show() #customer with 18 yrs of age are 4 members and person with 19 yrs of age are 8 members and so on.....

plt.figure(figsize=(6,3))
plt.bar(data['Gender'],data['Annual Income (k$)'],width=0.2,color='purple')
plt.show()   #Male is having more annual income nearly 137 and female is having less annual income nearly 125

plt.figure(figsize=(8,4))
plt.hist(data['Spending Score (1-100)'],histtype='barstacked',color='darkblue')
plt.title('Number of customers spending score')
plt.xlabel('Spending Score')
plt.ylabel('Count of people')
plt.show()  #Nearly 16-17 customers spending score is between 0 to 16 or 17

plt.figure(figsize=(13,3))
count = 0
for x in [data['Age'],data['Annual Income (k$)'],data['Spending Score (1-100)']]:
    count+=1
    plt.subplot(1,4,count,frameon=True,facecolor='black',alpha=0.5)
    plt.subplots_adjust(hspace =0.7 , wspace = 0.5)
    sns.distplot(x,bins=20)
plt.show()

Data Preprocessing

Handle Missing values

#Check for missing values
missingvalues = data.isnull().sum()
missingvalues  #As we can see that all columns is not having any missing values

CustomerID                0
Gender                    0
Age                       0
Annual Income (k$)        0
Spending Score (1-100)    0
dtype: int64

Handle duplicate data

#Check for duplicate rows
duplicaterows = data[data.duplicated()]
duplicaterows  #As we can see that there are no duplicate rows

	CustomerID	Gender	Age	Annual Income (k$)	Spending Score (1-100)

pd.set_option('display.max_columns', None)  # Show all columns
pd.set_option('display.max_rows', None) 

duplicateani = data.duplicated(subset=['Annual Income (k$)'])
duplicateani #True indicates there are duplicate rows but no problem annual income can be same for more than one customers

0      False
1       True
2      False
3       True
4      False
5       True
6      False
7       True
8      False
9       True
10      True
11      True
12     False
13      True
14      True
15      True
16     False
17      True
18     False
19      True
20     False
21      True
22     False
23      True
24     False
25      True
26      True
27      True
28     False
29      True
30     False
31      True
32     False
33      True
34      True
35      True
36     False
37      True
38     False
39      True
40     False
41      True
42     False
43      True
44      True
45      True
46     False
47      True
48      True
49      True
50     False
51      True
52     False
53      True
54      True
55      True
56     False
57      True
58     False
59      True
60      True
61      True
62     False
63      True
64     False
65      True
66      True
67      True
68      True
69      True
70     False
71      True
72     False
73      True
74     False
75      True
76      True
77      True
78      True
79      True
80      True
81      True
82      True
83      True
84      True
85      True
86     False
87      True
88     False
89      True
90     False
91      True
92     False
93      True
94      True
95      True
96      True
97      True
98     False
99      True
100    False
101     True
102     True
103     True
104     True
105     True
106    False
107     True
108     True
109     True
110     True
111     True
112    False
113     True
114    False
115     True
116     True
117     True
118    False
119     True
120     True
121     True
122    False
123     True
124    False
125     True
126    False
127     True
128     True
129     True
130     True
131     True
132    False
133     True
134    False
135     True
136     True
137     True
138    False
139     True
140    False
141     True
142    False
143     True
144    False
145     True
146     True
147     True
148    False
149     True
150     True
151     True
152     True
153     True
154     True
155     True
156     True
157     True
158     True
159     True
160    False
161     True
162    False
163     True
164    False
165     True
166    False
167     True
168    False
169     True
170     True
171     True
172     True
173     True
174    False
175     True
176     True
177     True
178    False
179     True
180    False
181     True
182    False
183     True
184    False
185     True
186    False
187     True
188    False
189     True
190     True
191     True
192    False
193     True
194    False
195     True
196    False
197     True
198    False
199     True
dtype: bool

Check for Outliers

plt.figure(figsize=(5,4))
sns.boxplot(y=data['Annual Income (k$)'])
plt.title("Annual Income")
plt.show()  #Annual income outlier doesn't matter because some customers annual income may be more

plt.figure(figsize=(5,4))
sns.boxplot(y=data['Spending Score (1-100)'])
plt.title("Spending score")
plt.show() #There are no outliers in spending score

#drop the CustomerId column as it is not required
data.drop(columns="CustomerID",axis=1,inplace=True)

data

	Gender	Age	Annual Income (k$)	Spending Score (1-100)
0	Male	19	15	39
1	Male	21	15	81
2	Female	20	16	6
3	Female	23	16	77
4	Female	31	17	40
5	Female	22	17	76
6	Female	35	18	6
7	Female	23	18	94
8	Male	64	19	3
9	Female	30	19	72
10	Male	67	19	14
11	Female	35	19	99
12	Female	58	20	15
13	Female	24	20	77
14	Male	37	20	13
15	Male	22	20	79
16	Female	35	21	35
17	Male	20	21	66
18	Male	52	23	29
19	Female	35	23	98
20	Male	35	24	35
21	Male	25	24	73
22	Female	46	25	5
23	Male	31	25	73
24	Female	54	28	14
25	Male	29	28	82
26	Female	45	28	32
27	Male	35	28	61
28	Female	40	29	31
29	Female	23	29	87
30	Male	60	30	4
31	Female	21	30	73
32	Male	53	33	4
33	Male	18	33	92
34	Female	49	33	14
35	Female	21	33	81
36	Female	42	34	17
37	Female	30	34	73
38	Female	36	37	26
39	Female	20	37	75
40	Female	65	38	35
41	Male	24	38	92
42	Male	48	39	36
43	Female	31	39	61
44	Female	49	39	28
45	Female	24	39	65
46	Female	50	40	55
47	Female	27	40	47
48	Female	29	40	42
49	Female	31	40	42
50	Female	49	42	52
51	Male	33	42	60
52	Female	31	43	54
53	Male	59	43	60
54	Female	50	43	45
55	Male	47	43	41
56	Female	51	44	50
57	Male	69	44	46
58	Female	27	46	51
59	Male	53	46	46
60	Male	70	46	56
61	Male	19	46	55
62	Female	67	47	52
63	Female	54	47	59
64	Male	63	48	51
65	Male	18	48	59
66	Female	43	48	50
67	Female	68	48	48
68	Male	19	48	59
69	Female	32	48	47
70	Male	70	49	55
71	Female	47	49	42
72	Female	60	50	49
73	Female	60	50	56
74	Male	59	54	47
75	Male	26	54	54
76	Female	45	54	53
77	Male	40	54	48
78	Female	23	54	52
79	Female	49	54	42
80	Male	57	54	51
81	Male	38	54	55
82	Male	67	54	41
83	Female	46	54	44
84	Female	21	54	57
85	Male	48	54	46
86	Female	55	57	58
87	Female	22	57	55
88	Female	34	58	60
89	Female	50	58	46
90	Female	68	59	55
91	Male	18	59	41
92	Male	48	60	49
93	Female	40	60	40
94	Female	32	60	42
95	Male	24	60	52
96	Female	47	60	47
97	Female	27	60	50
98	Male	48	61	42
99	Male	20	61	49
100	Female	23	62	41
101	Female	49	62	48
102	Male	67	62	59
103	Male	26	62	55
104	Male	49	62	56
105	Female	21	62	42
106	Female	66	63	50
107	Male	54	63	46
108	Male	68	63	43
109	Male	66	63	48
110	Male	65	63	52
111	Female	19	63	54
112	Female	38	64	42
113	Male	19	64	46
114	Female	18	65	48
115	Female	19	65	50
116	Female	63	65	43
117	Female	49	65	59
118	Female	51	67	43
119	Female	50	67	57
120	Male	27	67	56
121	Female	38	67	40
122	Female	40	69	58
123	Male	39	69	91
124	Female	23	70	29
125	Female	31	70	77
126	Male	43	71	35
127	Male	40	71	95
128	Male	59	71	11
129	Male	38	71	75
130	Male	47	71	9
131	Male	39	71	75
132	Female	25	72	34
133	Female	31	72	71
134	Male	20	73	5
135	Female	29	73	88
136	Female	44	73	7
137	Male	32	73	73
138	Male	19	74	10
139	Female	35	74	72
140	Female	57	75	5
141	Male	32	75	93
142	Female	28	76	40
143	Female	32	76	87
144	Male	25	77	12
145	Male	28	77	97
146	Male	48	77	36
147	Female	32	77	74
148	Female	34	78	22
149	Male	34	78	90
150	Male	43	78	17
151	Male	39	78	88
152	Female	44	78	20
153	Female	38	78	76
154	Female	47	78	16
155	Female	27	78	89
156	Male	37	78	1
157	Female	30	78	78
158	Male	34	78	1
159	Female	30	78	73
160	Female	56	79	35
161	Female	29	79	83
162	Male	19	81	5
163	Female	31	81	93
164	Male	50	85	26
165	Female	36	85	75
166	Male	42	86	20
167	Female	33	86	95
168	Female	36	87	27
169	Male	32	87	63
170	Male	40	87	13
171	Male	28	87	75
172	Male	36	87	10
173	Male	36	87	92
174	Female	52	88	13
175	Female	30	88	86
176	Male	58	88	15
177	Male	27	88	69
178	Male	59	93	14
179	Male	35	93	90
180	Female	37	97	32
181	Female	32	97	86
182	Male	46	98	15
183	Female	29	98	88
184	Female	41	99	39
185	Male	30	99	97
186	Female	54	101	24
187	Male	28	101	68
188	Female	41	103	17
189	Female	36	103	85
190	Female	34	103	23
191	Female	32	103	69
192	Male	33	113	8
193	Female	38	113	91
194	Female	47	120	16
195	Female	35	120	79
196	Female	45	126	28
197	Male	32	126	74
198	Male	32	137	18
199	Male	30	137	83

K-Means Clustering

K-Means clustering is a type of unsupervised machine learning algorithm that is used when we have unlabeled data(i,e the data without defined categories or groups). The goal of this algorithm is to find groups in the data, with the number of groups represented by the variable k

For this algorithm to work, the number of clusters has to be known before. The K in K-means refers to number of clusters.

Customer segmentation according to Age and Spending Score

sns.displot(data,x='Age',element="step",hue='Age',legend=False,palette="dark") #Mostly customers are between 30-35 years of age

<seaborn.axisgrid.FacetGrid at 0x1e6cf5917d0>

sns.scatterplot(x=data['Age'],y=data['Spending Score (1-100)'],hue=data['Age'],size=data['Age'])

<Axes: xlabel='Age', ylabel='Spending Score (1-100)'>

d1 = data[['Age','Spending Score (1-100)']]
d1

	Age	Spending Score (1-100)
0	19	39
1	21	81
2	20	6
3	23	77
4	31	40
5	22	76
6	35	6
7	23	94
8	64	3
9	30	72
10	67	14
11	35	99
12	58	15
13	24	77
14	37	13
15	22	79
16	35	35
17	20	66
18	52	29
19	35	98
20	35	35
21	25	73
22	46	5
23	31	73
24	54	14
25	29	82
26	45	32
27	35	61
28	40	31
29	23	87
30	60	4
31	21	73
32	53	4
33	18	92
34	49	14
35	21	81
36	42	17
37	30	73
38	36	26
39	20	75
40	65	35
41	24	92
42	48	36
43	31	61
44	49	28
45	24	65
46	50	55
47	27	47
48	29	42
49	31	42
50	49	52
51	33	60
52	31	54
53	59	60
54	50	45
55	47	41
56	51	50
57	69	46
58	27	51
59	53	46
60	70	56
61	19	55
62	67	52
63	54	59
64	63	51
65	18	59
66	43	50
67	68	48
68	19	59
69	32	47
70	70	55
71	47	42
72	60	49
73	60	56
74	59	47
75	26	54
76	45	53
77	40	48
78	23	52
79	49	42
80	57	51
81	38	55
82	67	41
83	46	44
84	21	57
85	48	46
86	55	58
87	22	55
88	34	60
89	50	46
90	68	55
91	18	41
92	48	49
93	40	40
94	32	42
95	24	52
96	47	47
97	27	50
98	48	42
99	20	49
100	23	41
101	49	48
102	67	59
103	26	55
104	49	56
105	21	42
106	66	50
107	54	46
108	68	43
109	66	48
110	65	52
111	19	54
112	38	42
113	19	46
114	18	48
115	19	50
116	63	43
117	49	59
118	51	43
119	50	57
120	27	56
121	38	40
122	40	58
123	39	91
124	23	29
125	31	77
126	43	35
127	40	95
128	59	11
129	38	75
130	47	9
131	39	75
132	25	34
133	31	71
134	20	5
135	29	88
136	44	7
137	32	73
138	19	10
139	35	72
140	57	5
141	32	93
142	28	40
143	32	87
144	25	12
145	28	97
146	48	36
147	32	74
148	34	22
149	34	90
150	43	17
151	39	88
152	44	20
153	38	76
154	47	16
155	27	89
156	37	1
157	30	78
158	34	1
159	30	73
160	56	35
161	29	83
162	19	5
163	31	93
164	50	26
165	36	75
166	42	20
167	33	95
168	36	27
169	32	63
170	40	13
171	28	75
172	36	10
173	36	92
174	52	13
175	30	86
176	58	15
177	27	69
178	59	14
179	35	90
180	37	32
181	32	86
182	46	15
183	29	88
184	41	39
185	30	97
186	54	24
187	28	68
188	41	17
189	36	85
190	34	23
191	32	69
192	33	8
193	38	91
194	47	16
195	35	79
196	45	28
197	32	74
198	32	18
199	30	83

sc = StandardScaler()

Scaling all the values

Data1 = sc.fit_transform(d1)

Data1

array([[-1.42456879, -0.43480148],
       [-1.28103541,  1.19570407],
       [-1.3528021 , -1.71591298],
       [-1.13750203,  1.04041783],
       [-0.56336851, -0.39597992],
       [-1.20926872,  1.00159627],
       [-0.27630176, -1.71591298],
       [-1.13750203,  1.70038436],
       [ 1.80493225, -1.83237767],
       [-0.6351352 ,  0.84631002],
       [ 2.02023231, -1.4053405 ],
       [-0.27630176,  1.89449216],
       [ 1.37433211, -1.36651894],
       [-1.06573534,  1.04041783],
       [-0.13276838, -1.44416206],
       [-1.20926872,  1.11806095],
       [-0.27630176, -0.59008772],
       [-1.3528021 ,  0.61338066],
       [ 0.94373197, -0.82301709],
       [-0.27630176,  1.8556706 ],
       [-0.27630176, -0.59008772],
       [-0.99396865,  0.88513158],
       [ 0.51313183, -1.75473454],
       [-0.56336851,  0.88513158],
       [ 1.08726535, -1.4053405 ],
       [-0.70690189,  1.23452563],
       [ 0.44136514, -0.7065524 ],
       [-0.27630176,  0.41927286],
       [ 0.08253169, -0.74537397],
       [-1.13750203,  1.42863343],
       [ 1.51786549, -1.7935561 ],
       [-1.28103541,  0.88513158],
       [ 1.01549866, -1.7935561 ],
       [-1.49633548,  1.62274124],
       [ 0.7284319 , -1.4053405 ],
       [-1.28103541,  1.19570407],
       [ 0.22606507, -1.28887582],
       [-0.6351352 ,  0.88513158],
       [-0.20453507, -0.93948177],
       [-1.3528021 ,  0.96277471],
       [ 1.87669894, -0.59008772],
       [-1.06573534,  1.62274124],
       [ 0.65666521, -0.55126616],
       [-0.56336851,  0.41927286],
       [ 0.7284319 , -0.86183865],
       [-1.06573534,  0.5745591 ],
       [ 0.80019859,  0.18634349],
       [-0.85043527, -0.12422899],
       [-0.70690189, -0.3183368 ],
       [-0.56336851, -0.3183368 ],
       [ 0.7284319 ,  0.06987881],
       [-0.41983513,  0.38045129],
       [-0.56336851,  0.14752193],
       [ 1.4460988 ,  0.38045129],
       [ 0.80019859, -0.20187212],
       [ 0.58489852, -0.35715836],
       [ 0.87196528, -0.00776431],
       [ 2.16376569, -0.16305055],
       [-0.85043527,  0.03105725],
       [ 1.01549866, -0.16305055],
       [ 2.23553238,  0.22516505],
       [-1.42456879,  0.18634349],
       [ 2.02023231,  0.06987881],
       [ 1.08726535,  0.34162973],
       [ 1.73316556,  0.03105725],
       [-1.49633548,  0.34162973],
       [ 0.29783176, -0.00776431],
       [ 2.091999  , -0.08540743],
       [-1.42456879,  0.34162973],
       [-0.49160182, -0.12422899],
       [ 2.23553238,  0.18634349],
       [ 0.58489852, -0.3183368 ],
       [ 1.51786549, -0.04658587],
       [ 1.51786549,  0.22516505],
       [ 1.4460988 , -0.12422899],
       [-0.92220196,  0.14752193],
       [ 0.44136514,  0.10870037],
       [ 0.08253169, -0.08540743],
       [-1.13750203,  0.06987881],
       [ 0.7284319 , -0.3183368 ],
       [ 1.30256542,  0.03105725],
       [-0.06100169,  0.18634349],
       [ 2.02023231, -0.35715836],
       [ 0.51313183, -0.24069368],
       [-1.28103541,  0.26398661],
       [ 0.65666521, -0.16305055],
       [ 1.15903204,  0.30280817],
       [-1.20926872,  0.18634349],
       [-0.34806844,  0.38045129],
       [ 0.80019859, -0.16305055],
       [ 2.091999  ,  0.18634349],
       [-1.49633548, -0.35715836],
       [ 0.65666521, -0.04658587],
       [ 0.08253169, -0.39597992],
       [-0.49160182, -0.3183368 ],
       [-1.06573534,  0.06987881],
       [ 0.58489852, -0.12422899],
       [-0.85043527, -0.00776431],
       [ 0.65666521, -0.3183368 ],
       [-1.3528021 , -0.04658587],
       [-1.13750203, -0.35715836],
       [ 0.7284319 , -0.08540743],
       [ 2.02023231,  0.34162973],
       [-0.92220196,  0.18634349],
       [ 0.7284319 ,  0.22516505],
       [-1.28103541, -0.3183368 ],
       [ 1.94846562, -0.00776431],
       [ 1.08726535, -0.16305055],
       [ 2.091999  , -0.27951524],
       [ 1.94846562, -0.08540743],
       [ 1.87669894,  0.06987881],
       [-1.42456879,  0.14752193],
       [-0.06100169, -0.3183368 ],
       [-1.42456879, -0.16305055],
       [-1.49633548, -0.08540743],
       [-1.42456879, -0.00776431],
       [ 1.73316556, -0.27951524],
       [ 0.7284319 ,  0.34162973],
       [ 0.87196528, -0.27951524],
       [ 0.80019859,  0.26398661],
       [-0.85043527,  0.22516505],
       [-0.06100169, -0.39597992],
       [ 0.08253169,  0.30280817],
       [ 0.010765  ,  1.58391968],
       [-1.13750203, -0.82301709],
       [-0.56336851,  1.04041783],
       [ 0.29783176, -0.59008772],
       [ 0.08253169,  1.73920592],
       [ 1.4460988 , -1.52180518],
       [-0.06100169,  0.96277471],
       [ 0.58489852, -1.5994483 ],
       [ 0.010765  ,  0.96277471],
       [-0.99396865, -0.62890928],
       [-0.56336851,  0.80748846],
       [-1.3528021 , -1.75473454],
       [-0.70690189,  1.46745499],
       [ 0.36959845, -1.67709142],
       [-0.49160182,  0.88513158],
       [-1.42456879, -1.56062674],
       [-0.27630176,  0.84631002],
       [ 1.30256542, -1.75473454],
       [-0.49160182,  1.6615628 ],
       [-0.77866858, -0.39597992],
       [-0.49160182,  1.42863343],
       [-0.99396865, -1.48298362],
       [-0.77866858,  1.81684904],
       [ 0.65666521, -0.55126616],
       [-0.49160182,  0.92395314],
       [-0.34806844, -1.09476801],
       [-0.34806844,  1.54509812],
       [ 0.29783176, -1.28887582],
       [ 0.010765  ,  1.46745499],
       [ 0.36959845, -1.17241113],
       [-0.06100169,  1.00159627],
       [ 0.58489852, -1.32769738],
       [-0.85043527,  1.50627656],
       [-0.13276838, -1.91002079],
       [-0.6351352 ,  1.07923939],
       [-0.34806844, -1.91002079],
       [-0.6351352 ,  0.88513158],
       [ 1.23079873, -0.59008772],
       [-0.70690189,  1.27334719],
       [-1.42456879, -1.75473454],
       [-0.56336851,  1.6615628 ],
       [ 0.80019859, -0.93948177],
       [-0.20453507,  0.96277471],
       [ 0.22606507, -1.17241113],
       [-0.41983513,  1.73920592],
       [-0.20453507, -0.90066021],
       [-0.49160182,  0.49691598],
       [ 0.08253169, -1.44416206],
       [-0.77866858,  0.96277471],
       [-0.20453507, -1.56062674],
       [-0.20453507,  1.62274124],
       [ 0.94373197, -1.44416206],
       [-0.6351352 ,  1.38981187],
       [ 1.37433211, -1.36651894],
       [-0.85043527,  0.72984534],
       [ 1.4460988 , -1.4053405 ],
       [-0.27630176,  1.54509812],
       [-0.13276838, -0.7065524 ],
       [-0.49160182,  1.38981187],
       [ 0.51313183, -1.36651894],
       [-0.70690189,  1.46745499],
       [ 0.15429838, -0.43480148],
       [-0.6351352 ,  1.81684904],
       [ 1.08726535, -1.01712489],
       [-0.77866858,  0.69102378],
       [ 0.15429838, -1.28887582],
       [-0.20453507,  1.35099031],
       [-0.34806844, -1.05594645],
       [-0.49160182,  0.72984534],
       [-0.41983513, -1.63826986],
       [-0.06100169,  1.58391968],
       [ 0.58489852, -1.32769738],
       [-0.27630176,  1.11806095],
       [ 0.44136514, -0.86183865],
       [-0.49160182,  0.92395314],
       [-0.49160182, -1.25005425],
       [-0.6351352 ,  1.27334719]])

Finding the number of clusters using Elbow method

Elbow method is used to find optimal K value via graphical representation. It works by finding Within cluster sum of squares(WCSS) i.e, the sum of the square of the distance between the cluster points and a cluster centroid

The elbow graph shows WCSS values on y-axis corresponding to K values on x-axis. When we see the elbow shape in the graph, we pick the K value where elbow gets created in the graph

from sklearn.cluster import KMeans

wcss = []
for i in range(1,10):
    km = KMeans(n_clusters=i)
    km.fit_predict(Data1)
    a = km.inertia_
    wcss.append(a)

import plotly.express as px
import plotly.graph_objects as go

b = range(1,10)
px.line(x=b,y=wcss)

From the above graph it can be seen the number of clusters (K value) is 4

Building the model

km = KMeans(n_clusters=4)

prediction1 = km.fit_predict(Data1)
prediction1

array([2, 0, 2, 0, 2, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 2, 0, 1, 0, 2, 0,
       1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 2, 0, 3, 0, 3, 0,
       1, 0, 3, 2, 2, 2, 3, 0, 2, 3, 3, 3, 3, 3, 2, 3, 3, 2, 3, 3, 3, 2,
       3, 3, 2, 2, 3, 3, 3, 3, 3, 2, 3, 2, 2, 3, 3, 2, 3, 3, 2, 3, 3, 2,
       0, 3, 3, 2, 3, 2, 2, 2, 3, 2, 3, 2, 2, 3, 3, 2, 3, 2, 3, 3, 3, 3,
       3, 2, 2, 2, 2, 2, 3, 3, 3, 3, 2, 2, 0, 0, 2, 0, 1, 0, 1, 0, 1, 0,
       2, 0, 2, 0, 1, 0, 2, 0, 1, 0, 2, 0, 2, 0, 3, 0, 2, 0, 1, 0, 1, 0,
       1, 0, 1, 0, 1, 0, 3, 0, 2, 0, 1, 0, 1, 0, 2, 0, 1, 0, 1, 0, 1, 0,
       1, 0, 1, 0, 2, 0, 1, 0, 2, 0, 1, 0, 1, 0, 2, 0, 1, 0, 1, 0, 1, 0,
       2, 0])

data['age_cluster'] = prediction1
data

	Gender	Age	Annual Income (k$)	Spending Score (1-100)	age_cluster
0	Male	19	15	39	2
1	Male	21	15	81	0
2	Female	20	16	6	2
3	Female	23	16	77	0
4	Female	31	17	40	2
5	Female	22	17	76	0
6	Female	35	18	6	1
7	Female	23	18	94	0
8	Male	64	19	3	1
9	Female	30	19	72	0
10	Male	67	19	14	1
11	Female	35	19	99	0
12	Female	58	20	15	1
13	Female	24	20	77	0
14	Male	37	20	13	1
15	Male	22	20	79	0
16	Female	35	21	35	2
17	Male	20	21	66	0
18	Male	52	23	29	1
19	Female	35	23	98	0
20	Male	35	24	35	2
21	Male	25	24	73	0
22	Female	46	25	5	1
23	Male	31	25	73	0
24	Female	54	28	14	1
25	Male	29	28	82	0
26	Female	45	28	32	1
27	Male	35	28	61	0
28	Female	40	29	31	1
29	Female	23	29	87	0
30	Male	60	30	4	1
31	Female	21	30	73	0
32	Male	53	33	4	1
33	Male	18	33	92	0
34	Female	49	33	14	1
35	Female	21	33	81	0
36	Female	42	34	17	1
37	Female	30	34	73	0
38	Female	36	37	26	2
39	Female	20	37	75	0
40	Female	65	38	35	3
41	Male	24	38	92	0
42	Male	48	39	36	3
43	Female	31	39	61	0
44	Female	49	39	28	1
45	Female	24	39	65	0
46	Female	50	40	55	3
47	Female	27	40	47	2
48	Female	29	40	42	2
49	Female	31	40	42	2
50	Female	49	42	52	3
51	Male	33	42	60	0
52	Female	31	43	54	2
53	Male	59	43	60	3
54	Female	50	43	45	3
55	Male	47	43	41	3
56	Female	51	44	50	3
57	Male	69	44	46	3
58	Female	27	46	51	2
59	Male	53	46	46	3
60	Male	70	46	56	3
61	Male	19	46	55	2
62	Female	67	47	52	3
63	Female	54	47	59	3
64	Male	63	48	51	3
65	Male	18	48	59	2
66	Female	43	48	50	3
67	Female	68	48	48	3
68	Male	19	48	59	2
69	Female	32	48	47	2
70	Male	70	49	55	3
71	Female	47	49	42	3
72	Female	60	50	49	3
73	Female	60	50	56	3
74	Male	59	54	47	3
75	Male	26	54	54	2
76	Female	45	54	53	3
77	Male	40	54	48	2
78	Female	23	54	52	2
79	Female	49	54	42	3
80	Male	57	54	51	3
81	Male	38	54	55	2
82	Male	67	54	41	3
83	Female	46	54	44	3
84	Female	21	54	57	2
85	Male	48	54	46	3
86	Female	55	57	58	3
87	Female	22	57	55	2
88	Female	34	58	60	0
89	Female	50	58	46	3
90	Female	68	59	55	3
91	Male	18	59	41	2
92	Male	48	60	49	3
93	Female	40	60	40	2
94	Female	32	60	42	2
95	Male	24	60	52	2
96	Female	47	60	47	3
97	Female	27	60	50	2
98	Male	48	61	42	3
99	Male	20	61	49	2
100	Female	23	62	41	2
101	Female	49	62	48	3
102	Male	67	62	59	3
103	Male	26	62	55	2
104	Male	49	62	56	3
105	Female	21	62	42	2
106	Female	66	63	50	3
107	Male	54	63	46	3
108	Male	68	63	43	3
109	Male	66	63	48	3
110	Male	65	63	52	3
111	Female	19	63	54	2
112	Female	38	64	42	2
113	Male	19	64	46	2
114	Female	18	65	48	2
115	Female	19	65	50	2
116	Female	63	65	43	3
117	Female	49	65	59	3
118	Female	51	67	43	3
119	Female	50	67	57	3
120	Male	27	67	56	2
121	Female	38	67	40	2
122	Female	40	69	58	0
123	Male	39	69	91	0
124	Female	23	70	29	2
125	Female	31	70	77	0
126	Male	43	71	35	1
127	Male	40	71	95	0
128	Male	59	71	11	1
129	Male	38	71	75	0
130	Male	47	71	9	1
131	Male	39	71	75	0
132	Female	25	72	34	2
133	Female	31	72	71	0
134	Male	20	73	5	2
135	Female	29	73	88	0
136	Female	44	73	7	1
137	Male	32	73	73	0
138	Male	19	74	10	2
139	Female	35	74	72	0
140	Female	57	75	5	1
141	Male	32	75	93	0
142	Female	28	76	40	2
143	Female	32	76	87	0
144	Male	25	77	12	2
145	Male	28	77	97	0
146	Male	48	77	36	3
147	Female	32	77	74	0
148	Female	34	78	22	2
149	Male	34	78	90	0
150	Male	43	78	17	1
151	Male	39	78	88	0
152	Female	44	78	20	1
153	Female	38	78	76	0
154	Female	47	78	16	1
155	Female	27	78	89	0
156	Male	37	78	1	1
157	Female	30	78	78	0
158	Male	34	78	1	1
159	Female	30	78	73	0
160	Female	56	79	35	3
161	Female	29	79	83	0
162	Male	19	81	5	2
163	Female	31	81	93	0
164	Male	50	85	26	1
165	Female	36	85	75	0
166	Male	42	86	20	1
167	Female	33	86	95	0
168	Female	36	87	27	2
169	Male	32	87	63	0
170	Male	40	87	13	1
171	Male	28	87	75	0
172	Male	36	87	10	1
173	Male	36	87	92	0
174	Female	52	88	13	1
175	Female	30	88	86	0
176	Male	58	88	15	1
177	Male	27	88	69	0
178	Male	59	93	14	1
179	Male	35	93	90	0
180	Female	37	97	32	2
181	Female	32	97	86	0
182	Male	46	98	15	1
183	Female	29	98	88	0
184	Female	41	99	39	2
185	Male	30	99	97	0
186	Female	54	101	24	1
187	Male	28	101	68	0
188	Female	41	103	17	1
189	Female	36	103	85	0
190	Female	34	103	23	2
191	Female	32	103	69	0
192	Male	33	113	8	1
193	Female	38	113	91	0
194	Female	47	120	16	1
195	Female	35	120	79	0
196	Female	45	126	28	1
197	Male	32	126	74	0
198	Male	32	137	18	2
199	Male	30	137	83	0

da1 = data[data['age_cluster']==0]
da2 = data[data['age_cluster']==1]
da3 = data[data['age_cluster']==2]
da4 = data[data['age_cluster']==3]

fig = go.Figure()
fig.add_trace(go.Scatter(x=da1['Age'],y=da1['Spending Score (1-100)'],mode="markers"))
fig.add_trace(go.Scatter(x=da2['Age'],y=da2['Spending Score (1-100)'],mode="markers"))
fig.add_trace(go.Scatter(x=da3['Age'],y=da3['Spending Score (1-100)'],mode="markers"))
fig.add_trace(go.Scatter(x=da4['Age'],y=da4['Spending Score (1-100)'],mode="markers"))

Customer Segmentation according to Annual Income (k$) and Spending Score (1-100)

data['Annual Income (k$)'].describe()

count    200.000000
mean      60.560000
std       26.264721
min       15.000000
25%       41.500000
50%       61.500000
75%       78.000000
max      137.000000
Name: Annual Income (k$), dtype: float64

px.histogram(data,x='Annual Income (k$)') #Most customers have Annual Income 70-79 k dollars

sns.scatterplot(x=data['Annual Income (k$)'],y=data['Spending Score (1-100)'],hue=data['Annual Income (k$)'],size=data['Annual Income (k$)'])

<Axes: xlabel='Annual Income (k$)', ylabel='Spending Score (1-100)'>

d2 = data[['Annual Income (k$)','Spending Score (1-100)']]
d2

	Annual Income (k$)	Spending Score (1-100)
0	15	39
1	15	81
2	16	6
3	16	77
4	17	40
5	17	76
6	18	6
7	18	94
8	19	3
9	19	72
10	19	14
11	19	99
12	20	15
13	20	77
14	20	13
15	20	79
16	21	35
17	21	66
18	23	29
19	23	98
20	24	35
21	24	73
22	25	5
23	25	73
24	28	14
25	28	82
26	28	32
27	28	61
28	29	31
29	29	87
30	30	4
31	30	73
32	33	4
33	33	92
34	33	14
35	33	81
36	34	17
37	34	73
38	37	26
39	37	75
40	38	35
41	38	92
42	39	36
43	39	61
44	39	28
45	39	65
46	40	55
47	40	47
48	40	42
49	40	42
50	42	52
51	42	60
52	43	54
53	43	60
54	43	45
55	43	41
56	44	50
57	44	46
58	46	51
59	46	46
60	46	56
61	46	55
62	47	52
63	47	59
64	48	51
65	48	59
66	48	50
67	48	48
68	48	59
69	48	47
70	49	55
71	49	42
72	50	49
73	50	56
74	54	47
75	54	54
76	54	53
77	54	48
78	54	52
79	54	42
80	54	51
81	54	55
82	54	41
83	54	44
84	54	57
85	54	46
86	57	58
87	57	55
88	58	60
89	58	46
90	59	55
91	59	41
92	60	49
93	60	40
94	60	42
95	60	52
96	60	47
97	60	50
98	61	42
99	61	49
100	62	41
101	62	48
102	62	59
103	62	55
104	62	56
105	62	42
106	63	50
107	63	46
108	63	43
109	63	48
110	63	52
111	63	54
112	64	42
113	64	46
114	65	48
115	65	50
116	65	43
117	65	59
118	67	43
119	67	57
120	67	56
121	67	40
122	69	58
123	69	91
124	70	29
125	70	77
126	71	35
127	71	95
128	71	11
129	71	75
130	71	9
131	71	75
132	72	34
133	72	71
134	73	5
135	73	88
136	73	7
137	73	73
138	74	10
139	74	72
140	75	5
141	75	93
142	76	40
143	76	87
144	77	12
145	77	97
146	77	36
147	77	74
148	78	22
149	78	90
150	78	17
151	78	88
152	78	20
153	78	76
154	78	16
155	78	89
156	78	1
157	78	78
158	78	1
159	78	73
160	79	35
161	79	83
162	81	5
163	81	93
164	85	26
165	85	75
166	86	20
167	86	95
168	87	27
169	87	63
170	87	13
171	87	75
172	87	10
173	87	92
174	88	13
175	88	86
176	88	15
177	88	69
178	93	14
179	93	90
180	97	32
181	97	86
182	98	15
183	98	88
184	99	39
185	99	97
186	101	24
187	101	68
188	103	17
189	103	85
190	103	23
191	103	69
192	113	8
193	113	91
194	120	16
195	120	79
196	126	28
197	126	74
198	137	18
199	137	83

Scaling all the values

Data2 = sc.fit_transform(d2)

Data2

array([[-1.73899919, -0.43480148],
       [-1.73899919,  1.19570407],
       [-1.70082976, -1.71591298],
       [-1.70082976,  1.04041783],
       [-1.66266033, -0.39597992],
       [-1.66266033,  1.00159627],
       [-1.62449091, -1.71591298],
       [-1.62449091,  1.70038436],
       [-1.58632148, -1.83237767],
       [-1.58632148,  0.84631002],
       [-1.58632148, -1.4053405 ],
       [-1.58632148,  1.89449216],
       [-1.54815205, -1.36651894],
       [-1.54815205,  1.04041783],
       [-1.54815205, -1.44416206],
       [-1.54815205,  1.11806095],
       [-1.50998262, -0.59008772],
       [-1.50998262,  0.61338066],
       [-1.43364376, -0.82301709],
       [-1.43364376,  1.8556706 ],
       [-1.39547433, -0.59008772],
       [-1.39547433,  0.88513158],
       [-1.3573049 , -1.75473454],
       [-1.3573049 ,  0.88513158],
       [-1.24279661, -1.4053405 ],
       [-1.24279661,  1.23452563],
       [-1.24279661, -0.7065524 ],
       [-1.24279661,  0.41927286],
       [-1.20462718, -0.74537397],
       [-1.20462718,  1.42863343],
       [-1.16645776, -1.7935561 ],
       [-1.16645776,  0.88513158],
       [-1.05194947, -1.7935561 ],
       [-1.05194947,  1.62274124],
       [-1.05194947, -1.4053405 ],
       [-1.05194947,  1.19570407],
       [-1.01378004, -1.28887582],
       [-1.01378004,  0.88513158],
       [-0.89927175, -0.93948177],
       [-0.89927175,  0.96277471],
       [-0.86110232, -0.59008772],
       [-0.86110232,  1.62274124],
       [-0.82293289, -0.55126616],
       [-0.82293289,  0.41927286],
       [-0.82293289, -0.86183865],
       [-0.82293289,  0.5745591 ],
       [-0.78476346,  0.18634349],
       [-0.78476346, -0.12422899],
       [-0.78476346, -0.3183368 ],
       [-0.78476346, -0.3183368 ],
       [-0.70842461,  0.06987881],
       [-0.70842461,  0.38045129],
       [-0.67025518,  0.14752193],
       [-0.67025518,  0.38045129],
       [-0.67025518, -0.20187212],
       [-0.67025518, -0.35715836],
       [-0.63208575, -0.00776431],
       [-0.63208575, -0.16305055],
       [-0.55574689,  0.03105725],
       [-0.55574689, -0.16305055],
       [-0.55574689,  0.22516505],
       [-0.55574689,  0.18634349],
       [-0.51757746,  0.06987881],
       [-0.51757746,  0.34162973],
       [-0.47940803,  0.03105725],
       [-0.47940803,  0.34162973],
       [-0.47940803, -0.00776431],
       [-0.47940803, -0.08540743],
       [-0.47940803,  0.34162973],
       [-0.47940803, -0.12422899],
       [-0.4412386 ,  0.18634349],
       [-0.4412386 , -0.3183368 ],
       [-0.40306917, -0.04658587],
       [-0.40306917,  0.22516505],
       [-0.25039146, -0.12422899],
       [-0.25039146,  0.14752193],
       [-0.25039146,  0.10870037],
       [-0.25039146, -0.08540743],
       [-0.25039146,  0.06987881],
       [-0.25039146, -0.3183368 ],
       [-0.25039146,  0.03105725],
       [-0.25039146,  0.18634349],
       [-0.25039146, -0.35715836],
       [-0.25039146, -0.24069368],
       [-0.25039146,  0.26398661],
       [-0.25039146, -0.16305055],
       [-0.13588317,  0.30280817],
       [-0.13588317,  0.18634349],
       [-0.09771374,  0.38045129],
       [-0.09771374, -0.16305055],
       [-0.05954431,  0.18634349],
       [-0.05954431, -0.35715836],
       [-0.02137488, -0.04658587],
       [-0.02137488, -0.39597992],
       [-0.02137488, -0.3183368 ],
       [-0.02137488,  0.06987881],
       [-0.02137488, -0.12422899],
       [-0.02137488, -0.00776431],
       [ 0.01679455, -0.3183368 ],
       [ 0.01679455, -0.04658587],
       [ 0.05496398, -0.35715836],
       [ 0.05496398, -0.08540743],
       [ 0.05496398,  0.34162973],
       [ 0.05496398,  0.18634349],
       [ 0.05496398,  0.22516505],
       [ 0.05496398, -0.3183368 ],
       [ 0.09313341, -0.00776431],
       [ 0.09313341, -0.16305055],
       [ 0.09313341, -0.27951524],
       [ 0.09313341, -0.08540743],
       [ 0.09313341,  0.06987881],
       [ 0.09313341,  0.14752193],
       [ 0.13130284, -0.3183368 ],
       [ 0.13130284, -0.16305055],
       [ 0.16947227, -0.08540743],
       [ 0.16947227, -0.00776431],
       [ 0.16947227, -0.27951524],
       [ 0.16947227,  0.34162973],
       [ 0.24581112, -0.27951524],
       [ 0.24581112,  0.26398661],
       [ 0.24581112,  0.22516505],
       [ 0.24581112, -0.39597992],
       [ 0.32214998,  0.30280817],
       [ 0.32214998,  1.58391968],
       [ 0.36031941, -0.82301709],
       [ 0.36031941,  1.04041783],
       [ 0.39848884, -0.59008772],
       [ 0.39848884,  1.73920592],
       [ 0.39848884, -1.52180518],
       [ 0.39848884,  0.96277471],
       [ 0.39848884, -1.5994483 ],
       [ 0.39848884,  0.96277471],
       [ 0.43665827, -0.62890928],
       [ 0.43665827,  0.80748846],
       [ 0.4748277 , -1.75473454],
       [ 0.4748277 ,  1.46745499],
       [ 0.4748277 , -1.67709142],
       [ 0.4748277 ,  0.88513158],
       [ 0.51299713, -1.56062674],
       [ 0.51299713,  0.84631002],
       [ 0.55116656, -1.75473454],
       [ 0.55116656,  1.6615628 ],
       [ 0.58933599, -0.39597992],
       [ 0.58933599,  1.42863343],
       [ 0.62750542, -1.48298362],
       [ 0.62750542,  1.81684904],
       [ 0.62750542, -0.55126616],
       [ 0.62750542,  0.92395314],
       [ 0.66567484, -1.09476801],
       [ 0.66567484,  1.54509812],
       [ 0.66567484, -1.28887582],
       [ 0.66567484,  1.46745499],
       [ 0.66567484, -1.17241113],
       [ 0.66567484,  1.00159627],
       [ 0.66567484, -1.32769738],
       [ 0.66567484,  1.50627656],
       [ 0.66567484, -1.91002079],
       [ 0.66567484,  1.07923939],
       [ 0.66567484, -1.91002079],
       [ 0.66567484,  0.88513158],
       [ 0.70384427, -0.59008772],
       [ 0.70384427,  1.27334719],
       [ 0.78018313, -1.75473454],
       [ 0.78018313,  1.6615628 ],
       [ 0.93286085, -0.93948177],
       [ 0.93286085,  0.96277471],
       [ 0.97103028, -1.17241113],
       [ 0.97103028,  1.73920592],
       [ 1.00919971, -0.90066021],
       [ 1.00919971,  0.49691598],
       [ 1.00919971, -1.44416206],
       [ 1.00919971,  0.96277471],
       [ 1.00919971, -1.56062674],
       [ 1.00919971,  1.62274124],
       [ 1.04736914, -1.44416206],
       [ 1.04736914,  1.38981187],
       [ 1.04736914, -1.36651894],
       [ 1.04736914,  0.72984534],
       [ 1.23821628, -1.4053405 ],
       [ 1.23821628,  1.54509812],
       [ 1.390894  , -0.7065524 ],
       [ 1.390894  ,  1.38981187],
       [ 1.42906343, -1.36651894],
       [ 1.42906343,  1.46745499],
       [ 1.46723286, -0.43480148],
       [ 1.46723286,  1.81684904],
       [ 1.54357172, -1.01712489],
       [ 1.54357172,  0.69102378],
       [ 1.61991057, -1.28887582],
       [ 1.61991057,  1.35099031],
       [ 1.61991057, -1.05594645],
       [ 1.61991057,  0.72984534],
       [ 2.00160487, -1.63826986],
       [ 2.00160487,  1.58391968],
       [ 2.26879087, -1.32769738],
       [ 2.26879087,  1.11806095],
       [ 2.49780745, -0.86183865],
       [ 2.49780745,  0.92395314],
       [ 2.91767117, -1.25005425],
       [ 2.91767117,  1.27334719]])

Finding the number of clusters using elbow method

wcss = []
for i in range(1,10):
    km = KMeans(n_clusters=i)
    km.fit_predict(Data2)
    a = km.inertia_
    wcss.append(a)

wcss

[400.0,
 269.0167937490666,
 157.70400815035947,
 108.92131661364357,
 65.56840815571681,
 55.10377812115057,
 44.91271612308711,
 37.19952387597709,
 32.345837779566594]

c = range(1,10)
px.line(x=c,y=wcss)

From the above graph it can be seen that number of clusters (k value) is 5

Building the model

km = KMeans(n_clusters=5)

prediction2 = km.fit_predict(Data2)
prediction2

array([4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3,
       4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 1,
       4, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 2, 0, 1, 0, 2, 0, 2, 0,
       1, 0, 2, 0, 2, 0, 2, 0, 2, 0, 1, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0,
       2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0,
       2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0,
       2, 0])

data['salary_cluster'] = prediction2
data

	Gender	Age	Annual Income (k$)	Spending Score (1-100)	age_cluster	salary_cluster
0	Male	19	15	39	2	4
1	Male	21	15	81	0	3
2	Female	20	16	6	2	4
3	Female	23	16	77	0	3
4	Female	31	17	40	2	4
5	Female	22	17	76	0	3
6	Female	35	18	6	1	4
7	Female	23	18	94	0	3
8	Male	64	19	3	1	4
9	Female	30	19	72	0	3
10	Male	67	19	14	1	4
11	Female	35	19	99	0	3
12	Female	58	20	15	1	4
13	Female	24	20	77	0	3
14	Male	37	20	13	1	4
15	Male	22	20	79	0	3
16	Female	35	21	35	2	4
17	Male	20	21	66	0	3
18	Male	52	23	29	1	4
19	Female	35	23	98	0	3
20	Male	35	24	35	2	4
21	Male	25	24	73	0	3
22	Female	46	25	5	1	4
23	Male	31	25	73	0	3
24	Female	54	28	14	1	4
25	Male	29	28	82	0	3
26	Female	45	28	32	1	4
27	Male	35	28	61	0	3
28	Female	40	29	31	1	4
29	Female	23	29	87	0	3
30	Male	60	30	4	1	4
31	Female	21	30	73	0	3
32	Male	53	33	4	1	4
33	Male	18	33	92	0	3
34	Female	49	33	14	1	4
35	Female	21	33	81	0	3
36	Female	42	34	17	1	4
37	Female	30	34	73	0	3
38	Female	36	37	26	2	4
39	Female	20	37	75	0	3
40	Female	65	38	35	3	4
41	Male	24	38	92	0	3
42	Male	48	39	36	3	4
43	Female	31	39	61	0	1
44	Female	49	39	28	1	4
45	Female	24	39	65	0	3
46	Female	50	40	55	3	1
47	Female	27	40	47	2	1
48	Female	29	40	42	2	1
49	Female	31	40	42	2	1
50	Female	49	42	52	3	1
51	Male	33	42	60	0	1
52	Female	31	43	54	2	1
53	Male	59	43	60	3	1
54	Female	50	43	45	3	1
55	Male	47	43	41	3	1
56	Female	51	44	50	3	1
57	Male	69	44	46	3	1
58	Female	27	46	51	2	1
59	Male	53	46	46	3	1
60	Male	70	46	56	3	1
61	Male	19	46	55	2	1
62	Female	67	47	52	3	1
63	Female	54	47	59	3	1
64	Male	63	48	51	3	1
65	Male	18	48	59	2	1
66	Female	43	48	50	3	1
67	Female	68	48	48	3	1
68	Male	19	48	59	2	1
69	Female	32	48	47	2	1
70	Male	70	49	55	3	1
71	Female	47	49	42	3	1
72	Female	60	50	49	3	1
73	Female	60	50	56	3	1
74	Male	59	54	47	3	1
75	Male	26	54	54	2	1
76	Female	45	54	53	3	1
77	Male	40	54	48	2	1
78	Female	23	54	52	2	1
79	Female	49	54	42	3	1
80	Male	57	54	51	3	1
81	Male	38	54	55	2	1
82	Male	67	54	41	3	1
83	Female	46	54	44	3	1
84	Female	21	54	57	2	1
85	Male	48	54	46	3	1
86	Female	55	57	58	3	1
87	Female	22	57	55	2	1
88	Female	34	58	60	0	1
89	Female	50	58	46	3	1
90	Female	68	59	55	3	1
91	Male	18	59	41	2	1
92	Male	48	60	49	3	1
93	Female	40	60	40	2	1
94	Female	32	60	42	2	1
95	Male	24	60	52	2	1
96	Female	47	60	47	3	1
97	Female	27	60	50	2	1
98	Male	48	61	42	3	1
99	Male	20	61	49	2	1
100	Female	23	62	41	2	1
101	Female	49	62	48	3	1
102	Male	67	62	59	3	1
103	Male	26	62	55	2	1
104	Male	49	62	56	3	1
105	Female	21	62	42	2	1
106	Female	66	63	50	3	1
107	Male	54	63	46	3	1
108	Male	68	63	43	3	1
109	Male	66	63	48	3	1
110	Male	65	63	52	3	1
111	Female	19	63	54	2	1
112	Female	38	64	42	2	1
113	Male	19	64	46	2	1
114	Female	18	65	48	2	1
115	Female	19	65	50	2	1
116	Female	63	65	43	3	1
117	Female	49	65	59	3	1
118	Female	51	67	43	3	1
119	Female	50	67	57	3	1
120	Male	27	67	56	2	1
121	Female	38	67	40	2	1
122	Female	40	69	58	0	1
123	Male	39	69	91	0	0
124	Female	23	70	29	2	2
125	Female	31	70	77	0	0
126	Male	43	71	35	1	1
127	Male	40	71	95	0	0
128	Male	59	71	11	1	2
129	Male	38	71	75	0	0
130	Male	47	71	9	1	2
131	Male	39	71	75	0	0
132	Female	25	72	34	2	1
133	Female	31	72	71	0	0
134	Male	20	73	5	2	2
135	Female	29	73	88	0	0
136	Female	44	73	7	1	2
137	Male	32	73	73	0	0
138	Male	19	74	10	2	2
139	Female	35	74	72	0	0
140	Female	57	75	5	1	2
141	Male	32	75	93	0	0
142	Female	28	76	40	2	1
143	Female	32	76	87	0	0
144	Male	25	77	12	2	2
145	Male	28	77	97	0	0
146	Male	48	77	36	3	2
147	Female	32	77	74	0	0
148	Female	34	78	22	2	2
149	Male	34	78	90	0	0
150	Male	43	78	17	1	2
151	Male	39	78	88	0	0
152	Female	44	78	20	1	2
153	Female	38	78	76	0	0
154	Female	47	78	16	1	2
155	Female	27	78	89	0	0
156	Male	37	78	1	1	2
157	Female	30	78	78	0	0
158	Male	34	78	1	1	2
159	Female	30	78	73	0	0
160	Female	56	79	35	3	2
161	Female	29	79	83	0	0
162	Male	19	81	5	2	2
163	Female	31	81	93	0	0
164	Male	50	85	26	1	2
165	Female	36	85	75	0	0
166	Male	42	86	20	1	2
167	Female	33	86	95	0	0
168	Female	36	87	27	2	2
169	Male	32	87	63	0	0
170	Male	40	87	13	1	2
171	Male	28	87	75	0	0
172	Male	36	87	10	1	2
173	Male	36	87	92	0	0
174	Female	52	88	13	1	2
175	Female	30	88	86	0	0
176	Male	58	88	15	1	2
177	Male	27	88	69	0	0
178	Male	59	93	14	1	2
179	Male	35	93	90	0	0
180	Female	37	97	32	2	2
181	Female	32	97	86	0	0
182	Male	46	98	15	1	2
183	Female	29	98	88	0	0
184	Female	41	99	39	2	2
185	Male	30	99	97	0	0
186	Female	54	101	24	1	2
187	Male	28	101	68	0	0
188	Female	41	103	17	1	2
189	Female	36	103	85	0	0
190	Female	34	103	23	2	2
191	Female	32	103	69	0	0
192	Male	33	113	8	1	2
193	Female	38	113	91	0	0
194	Female	47	120	16	1	2
195	Female	35	120	79	0	0
196	Female	45	126	28	1	2
197	Male	32	126	74	0	0
198	Male	32	137	18	2	2
199	Male	30	137	83	0	0

ds1 = data[data['salary_cluster']==0]
ds2 = data[data['salary_cluster']==1]
ds3 = data[data['salary_cluster']==2]
ds4 = data[data['salary_cluster']==3]
ds5 = data[data['salary_cluster']==4]

fig = go.Figure()
fig.add_trace(go.Scatter(x=ds1['Annual Income (k$)'],y=ds1['Spending Score (1-100)'],mode="markers"))
fig.add_trace(go.Scatter(x=ds2['Annual Income (k$)'],y=ds2['Spending Score (1-100)'],mode="markers"))
fig.add_trace(go.Scatter(x=ds3['Annual Income (k$)'],y=ds3['Spending Score (1-100)'],mode="markers"))
fig.add_trace(go.Scatter(x=ds4['Annual Income (k$)'],y=ds4['Spending Score (1-100)'],mode="markers"))
fig.add_trace(go.Scatter(x=ds5['Annual Income (k$)'],y=ds5['Spending Score (1-100)'],mode="markers"))

Customer segmentation according to Age, Annual Income and Spending Score

d3 = data[['Age','Annual Income (k$)','Spending Score (1-100)']]
d3

	Age	Annual Income (k$)	Spending Score (1-100)
0	19	15	39
1	21	15	81
2	20	16	6
3	23	16	77
4	31	17	40
5	22	17	76
6	35	18	6
7	23	18	94
8	64	19	3
9	30	19	72
10	67	19	14
11	35	19	99
12	58	20	15
13	24	20	77
14	37	20	13
15	22	20	79
16	35	21	35
17	20	21	66
18	52	23	29
19	35	23	98
20	35	24	35
21	25	24	73
22	46	25	5
23	31	25	73
24	54	28	14
25	29	28	82
26	45	28	32
27	35	28	61
28	40	29	31
29	23	29	87
30	60	30	4
31	21	30	73
32	53	33	4
33	18	33	92
34	49	33	14
35	21	33	81
36	42	34	17
37	30	34	73
38	36	37	26
39	20	37	75
40	65	38	35
41	24	38	92
42	48	39	36
43	31	39	61
44	49	39	28
45	24	39	65
46	50	40	55
47	27	40	47
48	29	40	42
49	31	40	42
50	49	42	52
51	33	42	60
52	31	43	54
53	59	43	60
54	50	43	45
55	47	43	41
56	51	44	50
57	69	44	46
58	27	46	51
59	53	46	46
60	70	46	56
61	19	46	55
62	67	47	52
63	54	47	59
64	63	48	51
65	18	48	59
66	43	48	50
67	68	48	48
68	19	48	59
69	32	48	47
70	70	49	55
71	47	49	42
72	60	50	49
73	60	50	56
74	59	54	47
75	26	54	54
76	45	54	53
77	40	54	48
78	23	54	52
79	49	54	42
80	57	54	51
81	38	54	55
82	67	54	41
83	46	54	44
84	21	54	57
85	48	54	46
86	55	57	58
87	22	57	55
88	34	58	60
89	50	58	46
90	68	59	55
91	18	59	41
92	48	60	49
93	40	60	40
94	32	60	42
95	24	60	52
96	47	60	47
97	27	60	50
98	48	61	42
99	20	61	49
100	23	62	41
101	49	62	48
102	67	62	59
103	26	62	55
104	49	62	56
105	21	62	42
106	66	63	50
107	54	63	46
108	68	63	43
109	66	63	48
110	65	63	52
111	19	63	54
112	38	64	42
113	19	64	46
114	18	65	48
115	19	65	50
116	63	65	43
117	49	65	59
118	51	67	43
119	50	67	57
120	27	67	56
121	38	67	40
122	40	69	58
123	39	69	91
124	23	70	29
125	31	70	77
126	43	71	35
127	40	71	95
128	59	71	11
129	38	71	75
130	47	71	9
131	39	71	75
132	25	72	34
133	31	72	71
134	20	73	5
135	29	73	88
136	44	73	7
137	32	73	73
138	19	74	10
139	35	74	72
140	57	75	5
141	32	75	93
142	28	76	40
143	32	76	87
144	25	77	12
145	28	77	97
146	48	77	36
147	32	77	74
148	34	78	22
149	34	78	90
150	43	78	17
151	39	78	88
152	44	78	20
153	38	78	76
154	47	78	16
155	27	78	89
156	37	78	1
157	30	78	78
158	34	78	1
159	30	78	73
160	56	79	35
161	29	79	83
162	19	81	5
163	31	81	93
164	50	85	26
165	36	85	75
166	42	86	20
167	33	86	95
168	36	87	27
169	32	87	63
170	40	87	13
171	28	87	75
172	36	87	10
173	36	87	92
174	52	88	13
175	30	88	86
176	58	88	15
177	27	88	69
178	59	93	14
179	35	93	90
180	37	97	32
181	32	97	86
182	46	98	15
183	29	98	88
184	41	99	39
185	30	99	97
186	54	101	24
187	28	101	68
188	41	103	17
189	36	103	85
190	34	103	23
191	32	103	69
192	33	113	8
193	38	113	91
194	47	120	16
195	35	120	79
196	45	126	28
197	32	126	74
198	32	137	18
199	30	137	83

Scaling the values of d3

Data3 = sc.fit_transform(d3)
Data3

array([[-1.42456879, -1.73899919, -0.43480148],
       [-1.28103541, -1.73899919,  1.19570407],
       [-1.3528021 , -1.70082976, -1.71591298],
       [-1.13750203, -1.70082976,  1.04041783],
       [-0.56336851, -1.66266033, -0.39597992],
       [-1.20926872, -1.66266033,  1.00159627],
       [-0.27630176, -1.62449091, -1.71591298],
       [-1.13750203, -1.62449091,  1.70038436],
       [ 1.80493225, -1.58632148, -1.83237767],
       [-0.6351352 , -1.58632148,  0.84631002],
       [ 2.02023231, -1.58632148, -1.4053405 ],
       [-0.27630176, -1.58632148,  1.89449216],
       [ 1.37433211, -1.54815205, -1.36651894],
       [-1.06573534, -1.54815205,  1.04041783],
       [-0.13276838, -1.54815205, -1.44416206],
       [-1.20926872, -1.54815205,  1.11806095],
       [-0.27630176, -1.50998262, -0.59008772],
       [-1.3528021 , -1.50998262,  0.61338066],
       [ 0.94373197, -1.43364376, -0.82301709],
       [-0.27630176, -1.43364376,  1.8556706 ],
       [-0.27630176, -1.39547433, -0.59008772],
       [-0.99396865, -1.39547433,  0.88513158],
       [ 0.51313183, -1.3573049 , -1.75473454],
       [-0.56336851, -1.3573049 ,  0.88513158],
       [ 1.08726535, -1.24279661, -1.4053405 ],
       [-0.70690189, -1.24279661,  1.23452563],
       [ 0.44136514, -1.24279661, -0.7065524 ],
       [-0.27630176, -1.24279661,  0.41927286],
       [ 0.08253169, -1.20462718, -0.74537397],
       [-1.13750203, -1.20462718,  1.42863343],
       [ 1.51786549, -1.16645776, -1.7935561 ],
       [-1.28103541, -1.16645776,  0.88513158],
       [ 1.01549866, -1.05194947, -1.7935561 ],
       [-1.49633548, -1.05194947,  1.62274124],
       [ 0.7284319 , -1.05194947, -1.4053405 ],
       [-1.28103541, -1.05194947,  1.19570407],
       [ 0.22606507, -1.01378004, -1.28887582],
       [-0.6351352 , -1.01378004,  0.88513158],
       [-0.20453507, -0.89927175, -0.93948177],
       [-1.3528021 , -0.89927175,  0.96277471],
       [ 1.87669894, -0.86110232, -0.59008772],
       [-1.06573534, -0.86110232,  1.62274124],
       [ 0.65666521, -0.82293289, -0.55126616],
       [-0.56336851, -0.82293289,  0.41927286],
       [ 0.7284319 , -0.82293289, -0.86183865],
       [-1.06573534, -0.82293289,  0.5745591 ],
       [ 0.80019859, -0.78476346,  0.18634349],
       [-0.85043527, -0.78476346, -0.12422899],
       [-0.70690189, -0.78476346, -0.3183368 ],
       [-0.56336851, -0.78476346, -0.3183368 ],
       [ 0.7284319 , -0.70842461,  0.06987881],
       [-0.41983513, -0.70842461,  0.38045129],
       [-0.56336851, -0.67025518,  0.14752193],
       [ 1.4460988 , -0.67025518,  0.38045129],
       [ 0.80019859, -0.67025518, -0.20187212],
       [ 0.58489852, -0.67025518, -0.35715836],
       [ 0.87196528, -0.63208575, -0.00776431],
       [ 2.16376569, -0.63208575, -0.16305055],
       [-0.85043527, -0.55574689,  0.03105725],
       [ 1.01549866, -0.55574689, -0.16305055],
       [ 2.23553238, -0.55574689,  0.22516505],
       [-1.42456879, -0.55574689,  0.18634349],
       [ 2.02023231, -0.51757746,  0.06987881],
       [ 1.08726535, -0.51757746,  0.34162973],
       [ 1.73316556, -0.47940803,  0.03105725],
       [-1.49633548, -0.47940803,  0.34162973],
       [ 0.29783176, -0.47940803, -0.00776431],
       [ 2.091999  , -0.47940803, -0.08540743],
       [-1.42456879, -0.47940803,  0.34162973],
       [-0.49160182, -0.47940803, -0.12422899],
       [ 2.23553238, -0.4412386 ,  0.18634349],
       [ 0.58489852, -0.4412386 , -0.3183368 ],
       [ 1.51786549, -0.40306917, -0.04658587],
       [ 1.51786549, -0.40306917,  0.22516505],
       [ 1.4460988 , -0.25039146, -0.12422899],
       [-0.92220196, -0.25039146,  0.14752193],
       [ 0.44136514, -0.25039146,  0.10870037],
       [ 0.08253169, -0.25039146, -0.08540743],
       [-1.13750203, -0.25039146,  0.06987881],
       [ 0.7284319 , -0.25039146, -0.3183368 ],
       [ 1.30256542, -0.25039146,  0.03105725],
       [-0.06100169, -0.25039146,  0.18634349],
       [ 2.02023231, -0.25039146, -0.35715836],
       [ 0.51313183, -0.25039146, -0.24069368],
       [-1.28103541, -0.25039146,  0.26398661],
       [ 0.65666521, -0.25039146, -0.16305055],
       [ 1.15903204, -0.13588317,  0.30280817],
       [-1.20926872, -0.13588317,  0.18634349],
       [-0.34806844, -0.09771374,  0.38045129],
       [ 0.80019859, -0.09771374, -0.16305055],
       [ 2.091999  , -0.05954431,  0.18634349],
       [-1.49633548, -0.05954431, -0.35715836],
       [ 0.65666521, -0.02137488, -0.04658587],
       [ 0.08253169, -0.02137488, -0.39597992],
       [-0.49160182, -0.02137488, -0.3183368 ],
       [-1.06573534, -0.02137488,  0.06987881],
       [ 0.58489852, -0.02137488, -0.12422899],
       [-0.85043527, -0.02137488, -0.00776431],
       [ 0.65666521,  0.01679455, -0.3183368 ],
       [-1.3528021 ,  0.01679455, -0.04658587],
       [-1.13750203,  0.05496398, -0.35715836],
       [ 0.7284319 ,  0.05496398, -0.08540743],
       [ 2.02023231,  0.05496398,  0.34162973],
       [-0.92220196,  0.05496398,  0.18634349],
       [ 0.7284319 ,  0.05496398,  0.22516505],
       [-1.28103541,  0.05496398, -0.3183368 ],
       [ 1.94846562,  0.09313341, -0.00776431],
       [ 1.08726535,  0.09313341, -0.16305055],
       [ 2.091999  ,  0.09313341, -0.27951524],
       [ 1.94846562,  0.09313341, -0.08540743],
       [ 1.87669894,  0.09313341,  0.06987881],
       [-1.42456879,  0.09313341,  0.14752193],
       [-0.06100169,  0.13130284, -0.3183368 ],
       [-1.42456879,  0.13130284, -0.16305055],
       [-1.49633548,  0.16947227, -0.08540743],
       [-1.42456879,  0.16947227, -0.00776431],
       [ 1.73316556,  0.16947227, -0.27951524],
       [ 0.7284319 ,  0.16947227,  0.34162973],
       [ 0.87196528,  0.24581112, -0.27951524],
       [ 0.80019859,  0.24581112,  0.26398661],
       [-0.85043527,  0.24581112,  0.22516505],
       [-0.06100169,  0.24581112, -0.39597992],
       [ 0.08253169,  0.32214998,  0.30280817],
       [ 0.010765  ,  0.32214998,  1.58391968],
       [-1.13750203,  0.36031941, -0.82301709],
       [-0.56336851,  0.36031941,  1.04041783],
       [ 0.29783176,  0.39848884, -0.59008772],
       [ 0.08253169,  0.39848884,  1.73920592],
       [ 1.4460988 ,  0.39848884, -1.52180518],
       [-0.06100169,  0.39848884,  0.96277471],
       [ 0.58489852,  0.39848884, -1.5994483 ],
       [ 0.010765  ,  0.39848884,  0.96277471],
       [-0.99396865,  0.43665827, -0.62890928],
       [-0.56336851,  0.43665827,  0.80748846],
       [-1.3528021 ,  0.4748277 , -1.75473454],
       [-0.70690189,  0.4748277 ,  1.46745499],
       [ 0.36959845,  0.4748277 , -1.67709142],
       [-0.49160182,  0.4748277 ,  0.88513158],
       [-1.42456879,  0.51299713, -1.56062674],
       [-0.27630176,  0.51299713,  0.84631002],
       [ 1.30256542,  0.55116656, -1.75473454],
       [-0.49160182,  0.55116656,  1.6615628 ],
       [-0.77866858,  0.58933599, -0.39597992],
       [-0.49160182,  0.58933599,  1.42863343],
       [-0.99396865,  0.62750542, -1.48298362],
       [-0.77866858,  0.62750542,  1.81684904],
       [ 0.65666521,  0.62750542, -0.55126616],
       [-0.49160182,  0.62750542,  0.92395314],
       [-0.34806844,  0.66567484, -1.09476801],
       [-0.34806844,  0.66567484,  1.54509812],
       [ 0.29783176,  0.66567484, -1.28887582],
       [ 0.010765  ,  0.66567484,  1.46745499],
       [ 0.36959845,  0.66567484, -1.17241113],
       [-0.06100169,  0.66567484,  1.00159627],
       [ 0.58489852,  0.66567484, -1.32769738],
       [-0.85043527,  0.66567484,  1.50627656],
       [-0.13276838,  0.66567484, -1.91002079],
       [-0.6351352 ,  0.66567484,  1.07923939],
       [-0.34806844,  0.66567484, -1.91002079],
       [-0.6351352 ,  0.66567484,  0.88513158],
       [ 1.23079873,  0.70384427, -0.59008772],
       [-0.70690189,  0.70384427,  1.27334719],
       [-1.42456879,  0.78018313, -1.75473454],
       [-0.56336851,  0.78018313,  1.6615628 ],
       [ 0.80019859,  0.93286085, -0.93948177],
       [-0.20453507,  0.93286085,  0.96277471],
       [ 0.22606507,  0.97103028, -1.17241113],
       [-0.41983513,  0.97103028,  1.73920592],
       [-0.20453507,  1.00919971, -0.90066021],
       [-0.49160182,  1.00919971,  0.49691598],
       [ 0.08253169,  1.00919971, -1.44416206],
       [-0.77866858,  1.00919971,  0.96277471],
       [-0.20453507,  1.00919971, -1.56062674],
       [-0.20453507,  1.00919971,  1.62274124],
       [ 0.94373197,  1.04736914, -1.44416206],
       [-0.6351352 ,  1.04736914,  1.38981187],
       [ 1.37433211,  1.04736914, -1.36651894],
       [-0.85043527,  1.04736914,  0.72984534],
       [ 1.4460988 ,  1.23821628, -1.4053405 ],
       [-0.27630176,  1.23821628,  1.54509812],
       [-0.13276838,  1.390894  , -0.7065524 ],
       [-0.49160182,  1.390894  ,  1.38981187],
       [ 0.51313183,  1.42906343, -1.36651894],
       [-0.70690189,  1.42906343,  1.46745499],
       [ 0.15429838,  1.46723286, -0.43480148],
       [-0.6351352 ,  1.46723286,  1.81684904],
       [ 1.08726535,  1.54357172, -1.01712489],
       [-0.77866858,  1.54357172,  0.69102378],
       [ 0.15429838,  1.61991057, -1.28887582],
       [-0.20453507,  1.61991057,  1.35099031],
       [-0.34806844,  1.61991057, -1.05594645],
       [-0.49160182,  1.61991057,  0.72984534],
       [-0.41983513,  2.00160487, -1.63826986],
       [-0.06100169,  2.00160487,  1.58391968],
       [ 0.58489852,  2.26879087, -1.32769738],
       [-0.27630176,  2.26879087,  1.11806095],
       [ 0.44136514,  2.49780745, -0.86183865],
       [-0.49160182,  2.49780745,  0.92395314],
       [-0.49160182,  2.91767117, -1.25005425],
       [-0.6351352 ,  2.91767117,  1.27334719]])

Finding the number of clusters using elbow method

wcss = []
for i in range(1,10):
    km = KMeans(n_clusters=i)
    km.fit_predict(Data3)
    a = km.inertia_
    wcss.append(a)

wcss

[599.9999999999999,
 389.3861889564372,
 295.2122461555488,
 205.22514747675913,
 168.24758017556834,
 133.86842085478855,
 117.16911517019713,
 104.09784107506621,
 92.43257435420892]

d = range(1,10)
px.line(x=d,y=wcss)

From the graph above, it can be seen that the number of clusters is 6

Building the model

km = KMeans(n_clusters=6)

prediction3 = km.fit_predict(Data3)
prediction3

array([4, 4, 0, 4, 0, 4, 0, 4, 0, 4, 0, 4, 0, 4, 0, 4, 0, 4, 0, 4, 0, 4,
       0, 4, 0, 4, 0, 4, 0, 4, 0, 4, 0, 4, 0, 4, 0, 4, 0, 4, 5, 4, 0, 2,
       0, 4, 5, 2, 2, 2, 5, 2, 2, 5, 5, 5, 5, 5, 2, 5, 5, 2, 5, 5, 5, 2,
       5, 5, 2, 2, 5, 5, 5, 5, 5, 2, 5, 2, 2, 5, 5, 2, 5, 5, 2, 5, 5, 2,
       2, 5, 5, 2, 5, 2, 2, 2, 5, 2, 5, 2, 2, 5, 5, 2, 5, 2, 5, 5, 5, 5,
       5, 2, 2, 2, 2, 2, 5, 5, 5, 5, 2, 2, 2, 1, 2, 1, 3, 1, 3, 1, 3, 1,
       2, 1, 3, 1, 3, 1, 2, 1, 3, 1, 2, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1,
       3, 1, 3, 1, 3, 1, 5, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1,
       3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1,
       3, 1])

data['all_clusters'] = prediction3
data

	Gender	Age	Annual Income (k$)	Spending Score (1-100)	age_cluster	salary_cluster	all_clusters
0	Male	19	15	39	2	4	4
1	Male	21	15	81	0	3	4
2	Female	20	16	6	2	4	0
3	Female	23	16	77	0	3	4
4	Female	31	17	40	2	4	0
5	Female	22	17	76	0	3	4
6	Female	35	18	6	1	4	0
7	Female	23	18	94	0	3	4
8	Male	64	19	3	1	4	0
9	Female	30	19	72	0	3	4
10	Male	67	19	14	1	4	0
11	Female	35	19	99	0	3	4
12	Female	58	20	15	1	4	0
13	Female	24	20	77	0	3	4
14	Male	37	20	13	1	4	0
15	Male	22	20	79	0	3	4
16	Female	35	21	35	2	4	0
17	Male	20	21	66	0	3	4
18	Male	52	23	29	1	4	0
19	Female	35	23	98	0	3	4
20	Male	35	24	35	2	4	0
21	Male	25	24	73	0	3	4
22	Female	46	25	5	1	4	0
23	Male	31	25	73	0	3	4
24	Female	54	28	14	1	4	0
25	Male	29	28	82	0	3	4
26	Female	45	28	32	1	4	0
27	Male	35	28	61	0	3	4
28	Female	40	29	31	1	4	0
29	Female	23	29	87	0	3	4
30	Male	60	30	4	1	4	0
31	Female	21	30	73	0	3	4
32	Male	53	33	4	1	4	0
33	Male	18	33	92	0	3	4
34	Female	49	33	14	1	4	0
35	Female	21	33	81	0	3	4
36	Female	42	34	17	1	4	0
37	Female	30	34	73	0	3	4
38	Female	36	37	26	2	4	0
39	Female	20	37	75	0	3	4
40	Female	65	38	35	3	4	5
41	Male	24	38	92	0	3	4
42	Male	48	39	36	3	4	0
43	Female	31	39	61	0	1	2
44	Female	49	39	28	1	4	0
45	Female	24	39	65	0	3	4
46	Female	50	40	55	3	1	5
47	Female	27	40	47	2	1	2
48	Female	29	40	42	2	1	2
49	Female	31	40	42	2	1	2
50	Female	49	42	52	3	1	5
51	Male	33	42	60	0	1	2
52	Female	31	43	54	2	1	2
53	Male	59	43	60	3	1	5
54	Female	50	43	45	3	1	5
55	Male	47	43	41	3	1	5
56	Female	51	44	50	3	1	5
57	Male	69	44	46	3	1	5
58	Female	27	46	51	2	1	2
59	Male	53	46	46	3	1	5
60	Male	70	46	56	3	1	5
61	Male	19	46	55	2	1	2
62	Female	67	47	52	3	1	5
63	Female	54	47	59	3	1	5
64	Male	63	48	51	3	1	5
65	Male	18	48	59	2	1	2
66	Female	43	48	50	3	1	5
67	Female	68	48	48	3	1	5
68	Male	19	48	59	2	1	2
69	Female	32	48	47	2	1	2
70	Male	70	49	55	3	1	5
71	Female	47	49	42	3	1	5
72	Female	60	50	49	3	1	5
73	Female	60	50	56	3	1	5
74	Male	59	54	47	3	1	5
75	Male	26	54	54	2	1	2
76	Female	45	54	53	3	1	5
77	Male	40	54	48	2	1	2
78	Female	23	54	52	2	1	2
79	Female	49	54	42	3	1	5
80	Male	57	54	51	3	1	5
81	Male	38	54	55	2	1	2
82	Male	67	54	41	3	1	5
83	Female	46	54	44	3	1	5
84	Female	21	54	57	2	1	2
85	Male	48	54	46	3	1	5
86	Female	55	57	58	3	1	5
87	Female	22	57	55	2	1	2
88	Female	34	58	60	0	1	2
89	Female	50	58	46	3	1	5
90	Female	68	59	55	3	1	5
91	Male	18	59	41	2	1	2
92	Male	48	60	49	3	1	5
93	Female	40	60	40	2	1	2
94	Female	32	60	42	2	1	2
95	Male	24	60	52	2	1	2
96	Female	47	60	47	3	1	5
97	Female	27	60	50	2	1	2
98	Male	48	61	42	3	1	5
99	Male	20	61	49	2	1	2
100	Female	23	62	41	2	1	2
101	Female	49	62	48	3	1	5
102	Male	67	62	59	3	1	5
103	Male	26	62	55	2	1	2
104	Male	49	62	56	3	1	5
105	Female	21	62	42	2	1	2
106	Female	66	63	50	3	1	5
107	Male	54	63	46	3	1	5
108	Male	68	63	43	3	1	5
109	Male	66	63	48	3	1	5
110	Male	65	63	52	3	1	5
111	Female	19	63	54	2	1	2
112	Female	38	64	42	2	1	2
113	Male	19	64	46	2	1	2
114	Female	18	65	48	2	1	2
115	Female	19	65	50	2	1	2
116	Female	63	65	43	3	1	5
117	Female	49	65	59	3	1	5
118	Female	51	67	43	3	1	5
119	Female	50	67	57	3	1	5
120	Male	27	67	56	2	1	2
121	Female	38	67	40	2	1	2
122	Female	40	69	58	0	1	2
123	Male	39	69	91	0	0	1
124	Female	23	70	29	2	2	2
125	Female	31	70	77	0	0	1
126	Male	43	71	35	1	1	3
127	Male	40	71	95	0	0	1
128	Male	59	71	11	1	2	3
129	Male	38	71	75	0	0	1
130	Male	47	71	9	1	2	3
131	Male	39	71	75	0	0	1
132	Female	25	72	34	2	1	2
133	Female	31	72	71	0	0	1
134	Male	20	73	5	2	2	3
135	Female	29	73	88	0	0	1
136	Female	44	73	7	1	2	3
137	Male	32	73	73	0	0	1
138	Male	19	74	10	2	2	2
139	Female	35	74	72	0	0	1
140	Female	57	75	5	1	2	3
141	Male	32	75	93	0	0	1
142	Female	28	76	40	2	1	2
143	Female	32	76	87	0	0	1
144	Male	25	77	12	2	2	3
145	Male	28	77	97	0	0	1
146	Male	48	77	36	3	2	3
147	Female	32	77	74	0	0	1
148	Female	34	78	22	2	2	3
149	Male	34	78	90	0	0	1
150	Male	43	78	17	1	2	3
151	Male	39	78	88	0	0	1
152	Female	44	78	20	1	2	3
153	Female	38	78	76	0	0	1
154	Female	47	78	16	1	2	3
155	Female	27	78	89	0	0	1
156	Male	37	78	1	1	2	3
157	Female	30	78	78	0	0	1
158	Male	34	78	1	1	2	3
159	Female	30	78	73	0	0	1
160	Female	56	79	35	3	2	5
161	Female	29	79	83	0	0	1
162	Male	19	81	5	2	2	3
163	Female	31	81	93	0	0	1
164	Male	50	85	26	1	2	3
165	Female	36	85	75	0	0	1
166	Male	42	86	20	1	2	3
167	Female	33	86	95	0	0	1
168	Female	36	87	27	2	2	3
169	Male	32	87	63	0	0	1
170	Male	40	87	13	1	2	3
171	Male	28	87	75	0	0	1
172	Male	36	87	10	1	2	3
173	Male	36	87	92	0	0	1
174	Female	52	88	13	1	2	3
175	Female	30	88	86	0	0	1
176	Male	58	88	15	1	2	3
177	Male	27	88	69	0	0	1
178	Male	59	93	14	1	2	3
179	Male	35	93	90	0	0	1
180	Female	37	97	32	2	2	3
181	Female	32	97	86	0	0	1
182	Male	46	98	15	1	2	3
183	Female	29	98	88	0	0	1
184	Female	41	99	39	2	2	3
185	Male	30	99	97	0	0	1
186	Female	54	101	24	1	2	3
187	Male	28	101	68	0	0	1
188	Female	41	103	17	1	2	3
189	Female	36	103	85	0	0	1
190	Female	34	103	23	2	2	3
191	Female	32	103	69	0	0	1
192	Male	33	113	8	1	2	3
193	Female	38	113	91	0	0	1
194	Female	47	120	16	1	2	3
195	Female	35	120	79	0	0	1
196	Female	45	126	28	1	2	3
197	Male	32	126	74	0	0	1
198	Male	32	137	18	2	2	3
199	Male	30	137	83	0	0	1

df1 = data[data['all_clusters']==0]
df2 = data[data['all_clusters']==1]
df3 = data[data['all_clusters']==2]
df4 = data[data['all_clusters']==3]
df5 = data[data['all_clusters']==4]
df6 = data[data['all_clusters']==5]

fig = go.Figure()
fig.add_trace(go.Scatter3d(x=df1['Age'],y=df1['Annual Income (k$)'],z=df1['Spending Score (1-100)'],mode="markers"))
fig.add_trace(go.Scatter3d(x=df2['Age'],y=df2['Annual Income (k$)'],z=df2['Spending Score (1-100)'],mode="markers"))
fig.add_trace(go.Scatter3d(x=df3['Age'],y=df3['Annual Income (k$)'],z=df3['Spending Score (1-100)'],mode="markers"))
fig.add_trace(go.Scatter3d(x=df4['Age'],y=df4['Annual Income (k$)'],z=df4['Spending Score (1-100)'],mode="markers"))
fig.add_trace(go.Scatter3d(x=df5['Age'],y=df5['Annual Income (k$)'],z=df5['Spending Score (1-100)'],mode="markers"))
fig.add_trace(go.Scatter3d(x=df6['Age'],y=df6['Annual Income (k$)'],z=df6['Spending Score (1-100)'],mode="markers"))